Post-doctoral fellow in Physics; fast time-resolved 3D imaging with X-ray multi-projection imaging for advanced manufacturing

Subject description

The Division of Synchrotron Radiation Research (www.sljus.lu.se) is a part of the Department of Physics and has more than 40 employees. The focus of the research is on experimental studies of electronic, structural, and chemical properties of materials. The Division is developing a new activity focusing on magnetic properties. At the Division, we use and develop a wide range of large facility- and lab-based techniques. We are additionally engaged in the development of the MAX IV Laboratory in the fields of beamlines, experimental stations, techniques for Synchrotron radiation, and accelerator systems. MAX IV (http://www.maxiv.lu.se/) is a national large-scale research laboratory hosted by Lund University and situated within biking distance from the Department of Physics. It provides scientists from Sweden and the rest of the world with the most brilliant Synchrotron radiation source and instrumentation for research in areas such as materials science, structural biology, chemistry, and nanotechnology.

For more information about the main supervisors of the project, please see https://portal.research.lu.se/en/persons/pablo-villanueva-perez.

Work duties

This project aims to develop next-generation X-ray imaging methods capable of capturing 3D processes in real time during advanced manufacturing. By combining X-ray multi-projection imaging (XMPI), synchrotron radiation, and physics-informed machine learning, the project seeks to achieve volumetric imaging rates two to three orders of magnitude faster than current state-of-the-art approaches.

X-rays have a unique ability to investigate samples in their natural state, such as cells, proteins in solution, or electronic devices, potentially at resolutions up to the atomic level. The advent of diffraction-limited storage rings (MAX IV) and X-ray free-electron lasers (European XFEL) has opened a whole new spectrum of applications due to their unprecedented brilliance. This project aims to enable new spatiotemporal frontiers in time-resolved imaging while advancing manufacturing at previously unattainable speeds.

In particular, the candidate will (i) work on the development of novel methodologies for fast X-ray imaging based on X-ray multi-projection imaging, a technique being established at MAX IV, ii) contribute to the understanding of fast processes in additive manufacturing with novel methodology to enable 4D imaging two to three orders of magnitude faster than state-of-the-art techniques, and iii) contribute to the development of novel reconstruction algorithms to retrieve 4D (3D + time) datasets acquired using advanced X-ray imaging techniques. Such reconstruction algorithms entail merging state-of-the-art deep learning approaches with physical knowledge of the studied processes and X-ray imaging formation.

The developed approaches and algorithms will be tested, validated, and commissioned primarily at MAX IV (Sweden), as well as at ESRF-EBS (France) and SLS 2.0 (Switzerland).

The project includes opportunities for international travel and collaboration with leading synchrotron facilities and research institutes in Europe. Specifically, the project is framed within an international collaboration among ETH Zurich (Switzerland), the Paul Scherrer Institute (Switzerland), and Lund University (Sweden), including a planned 1–2 month research stay at PSI.

The project is experimental with a computational component. The successful candidate will have the opportunity to:

• Contribute to the development of X-ray multi-projection imaging, a technique capable of providing volumetric information 3 orders of magnitude faster than state-of-the-art time-resolved 3D imaging at synchrotron facilities.
• Apply XMPI to fast dynamics in advanced manufacturing.
• Develop new acquisition approaches and setups for fast 3D imaging.
• Develop novel algorithms for reconstructing high-speed 4D datasets by combining physical knowledge and state-of-the-art deep-learning approaches.
• Be part of an international collaboration to contribute to the understanding of key processes for advanced manufacturing through a novel approach.

The successful candidate will have opportunities to build an international network, lead beamtime experiments, supervise students, publish in high-impact journals, and develop an independent research profile.

The main duties involved in a post-doctoral position are to conduct research. Teaching may also be included, but up to no more than 20% of working hours. The position shall include the opportunity for three weeks of training in higher education teaching and learning.

Qualification requirements

Appointment to a post-doctoral position requires that the applicant has a PhD, or an international degree deemed equivalent to a PhD, within the subject of the position, completed no more than three years before the date of employment decision. Under special circumstances, the doctoral degree can have been completed earlier.

Additional requirements:

• Very good oral and written proficiency in English.
• The subject of the doctoral degree should be Physics, Engineering Physics, Applied Mathematics, Computer Science, Materials Science, Mechanical Engineering, or a closely related discipline.

Assessment criteria and other qualifications

This is a career development position primarily focused on research. The position is intended as an initial step in a career, and the assessment of the applicants will primarily be based on their research qualifications and potential as researchers. Particular emphasis will be placed on research skills within the subject.

For appointments to a post-doctoral position, the following shall form the assessment criteria:

• A good ability to develop and conduct high quality research.
• Teaching skills.

Additional assessment criteria:

• Experience in X-ray imaging.
• Experience in synchrotron radiation facilities.
• Experience with time-resolved 3D imaging.
• Experience in establishing acquisitions and reconstruction algorithms.
• Experience in machine learning and Big Data.
• Experience with programming and using version control software.
• Experience in studying advanced manufacturing or similar processes.
• Experience with in-situ and operando characterization.

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant’s experience and skills complement and strengthen ongoing research within the department, and how they stand to contribute to its future development.

Terms of employment

This is a full-time, fixed-term employment of a maximum of 2 years. The period of employment is determined in accordance with the agreement “Avtal om tidsbegränsad anställning som postdoktor” (“Agreement on fixed-term employment as a post-doctoral fellow”) between Lund University, SACO-S and OFR/S, dated 1st February 2022.

Instructions on how to apply

Applications shall be written in English and be compiled into a PDF-file containing:

• résumé/CV, including a list of publications,
• a general description of past research and future research interests (no more than three pages),
• contact information of at least two references,
• copy of the doctoral degree certificate, and other certificates/grades that you wish to be considered.